Summary of Auto-evolve: Enhancing Large Language Model’s Performance Via Self-reasoning Framework, by Krishna Aswani et al.
Auto-Evolve: Enhancing Large Language Model’s Performance via Self-Reasoning Framework
by Krishna Aswani, Huilin Lu, Pranav Patankar, Priya Dhalwani, Iris Tan, Jayant Ganeshmohan, Simon Lacasse
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Auto-Evolve, a novel framework that enables Large Language Models (LLMs) to self-create dynamic reasoning modules and downstream action plans. By eliminating the need for predefined templates, Auto-Evolve improves the flexibility of models in tackling diverse problems effectively. The authors evaluate Auto-Evolve on the challenging BigBench-Hard (BBH) dataset with various LLMs, including Claude 2.0, Claude 3 Sonnet, Mistral Large, and GPT 4. Compared to state-of-the-art (SOTA) prompt strategies like Chain-of-Thought (CoT), Auto-Evolve consistently outperforms them by up to 10.4% or an average of 7%. The framework’s innovations include dynamic reasoning module generation aligned with human reasoning paradigm and iterative refinement component that boosts performance by 2.8%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to help Large Language Models (LLMs) become better at solving problems. It’s called Auto-Evolve, and it lets the models create their own ways of thinking about a problem instead of following a set formula. This makes them more flexible and able to solve different types of problems. The researchers tested this method on a difficult dataset with several LLMs and found that it worked better than other methods by up to 10%. They also found that the models became even better at solving problems when they were allowed to refine their thinking over time. |
Keywords
» Artificial intelligence » Claude » Gpt » Prompt
